Finding the appropriate developer for a bug report, so called `Bug Triage', is one of the bottlenecks in the bug resolution process. To address this problem, many approaches have proposed various automatic bug triage techniques in recent studies. We argue that most previous studies focused on open source projects only and did not consider deep learning techniques. In this paper, we propose to use Convolutional Neural Network and word embedding to build an automatic bug triager. The results of the experiments applied to both industrial and open source projects reveal benefits of the automatic approach and suggest co-operation of human and automatic triagers. Our experience in integrating and operating the proposed system in an industrial development environment is also reported.

Although object-oriented programming (OOP) methodologies immensely promote reusable and well-factored decomposition of complex source code, legacy software systems often show symptoms of deteriorating design over time due to lack of maintenance. Software systems may have different business and application contexts, but most of these systems require similar maintenance mechanism of understanding, analysis and transformation. As a consequence, intensive re-engineering efforts based on the model driven approach can be effective ensuring that best practices are followed during maintenance and eventually reducing the development cost. In this paper, we suggest detailed framework of re-engineering which includes: (i) rigorous and automated source code analysis technique for identification, characterization and prioritization of most prominent and threatening design flaws in legacy software, (ii) migration of existing the code to aspect-oriented programming (AOP) code by exploiting current state of art for aspect mining mechanism and incorporating behavioral knowledge of cross-cutting concerns. To exemplify how the approach works a case study has been conducted to experimentally validate the idea and analyze the effect of process on specific software quality spectrum. An explicit analysis of prevalent work on the subject and their critical reviews are also presented to further enhance the recognition of proposed re-engineering framework.

There are performance deficiencies that hamper the deployment of Wireless Sensor Networks (WSNs) in critical monitoring applications. Such applications are characterized by considerable network load generated as a result of sensing some characteristics of the monitored system. Excessive packet collisions lead to packet losses and retransmissions, resulting in significant overhead costs and latency. In order to address this issue, we introduce a distributed and scalable scheduling access scheme that mitigates high data loss in data-intensive sensor networks and can also handle some mobility. Our approach alleviates transmission collisions by employing virtual grids that adopt Latin Squares characteristics to time slot assignments. We show that our algorithm derives conflict-free time slot allocation schedules without incurring global overhead in scheduling. Furthermore, we verify the effectiveness of our protocol by simulation experiments. The results demonstrate that our technique can efficiently handle sensor mobility with acceptable data loss, low packet delay, and low overhead.

This paper formulates and illustrates the integration of resource safety verification into a design methodology for development of verified and robust real-time embedded systems. Resource-related concerns are not closely linked with current xUML model-based software development although they are critical for embedded systems. We describe how to integrate resource analysis techniques into the early phase of an xUML-based development cycle. Our hybrid framework for resource safety verification combines static resource analysis and runtime monitoring. A case study based on an embedded controller for satellite simulation, TableSat, illustrates the benefits obtained by incorporating resource verification into design and combining static analysis and runtime monitoring.

In many emerging time-critical applications, the exact time of event occurrences may not be known. In such cases, events can be represented as a probabilistic occurrence within a time interval. Thus, monitoring of timing constraints, generally used in time- critical systems, needs to incorporate the uncertainty of event occurrences. In this paper, we propose mechanisms to monitor the satisfaction/violation of timing constraints that can be assessed probabilistically. We assume a uniform distribution of event occurrence within a time interval. Our proposed algorithm determines whether the probability that a timing constraint has been satisfied exceeds a specified threshold value. A confidence threshold is a minimum satisfaction probability of the timing constraint. A timing constraint is violated if the confidence threshold is not reached. We design an efficient monitoring algorithm for detecting timing violations of a set of timing constraints by finding the earliest expiration time (EET) for each timing constraint. Since it is critical to derive implicit constraints for early detection of violation of timing constraints, we present the derivation of the implicit constraints under uncertainty using an all-pairs shortest path algorithm. Further, we propose pruning techniques to discard unnecessary implicit constraints. We present the properties and proofs of our approach.

This paper addresses the problem of processing eventtiming queries over event streams where the uncertainty in the values of the timestamps is characterizable by histograms. We describe a stream-partitioning technique for checking the satisfaction of a probabilistic timing constraint upon event arrivals in a systematic way in order to delimit the "probing range" in event streams. This technique can be formalized as a probabilistic timing join (PTJoin) operator where the join condition is specified by a time window and a confidence threshold in our model. We present efficient PTJoin algorithms that tightly delimit the probing range and efficiently invalidate events in event streams.

This paper provides a comprehensive approach to the problem of monitoring timing constraints over event streams for which the timestamp values are inherently uncertain. We first propose a generic framework for capturing the early detection of the violation of timing constraints, based on the notion of probabilistic violation time. In doing so, we provide a systemic approach for deriving a set of necessary constraints at compilation time. Our work is innovative in that the framework is formulated to be "modular" with respect to the probability distributions on timestamp values. We demonstrate the applicability of the framework for two different timestamp models, Gaussian and histogram. The Gaussian model is appropriate for representing event timing from a wide variety of sensors with well-modelled physical noise characteristics; we show how we can efficiently derive the probabilistic violation time of timing constraints by exploiting the relation between the Gaussian distribution parameters. The histogram model can be used where the timestamps of events are available from measurements only as arbitrary probability distributions: we show how to derive an efficient timing constraint monitoring method for the histogram model

Increasingly, business applications need to capture consumers' complex preferences interactively and monitor those preferences by translating them into event-condition-action (ECA) rules and syntactically correct processing specification. An expressive event model to specify primitive and composite events that may involve timing constraints among events is critical to such applications. Relying on the work done in active databases and real-time systems, this research proposes a new composite event model based on real-time logic (RTL). The proposed event model does not require fixed event consumption policies and allows the users to represent the exact correlation of event instances in defining composite events. It also supports a wide-range of domain-specific temporal events and constraints, such as future events, time-constrained events, and relative events. This event model is validated within an electronic brokerage architecture that unbundles the required functionalities into three separable components - business rule manager, ECA rule manager, and event monitor - with well-defined interfaces. A proof-of-concept prototype was implemented in the Java programming language to demonstrate the expressiveness of the event model and the feasibility of the architecture. The performance of the composite event monitor was evaluated by varying the number of rules, event arrival rates, and type of composite events.

We propose an algorithm for monitoring timing constraints to satisfy confidence threshold requirements when there is uncertainty in the exact timing of event occurrences. In our model, a timed event trace is examined for possible satisfaction/violation with respect to a given set of timing constraints. Every event occurrence has a timestamp given by a time interval. Assuming that the time of occurrence is uniformly distributed over the time interval, our algorithm determines whether the probability that a timing constraint has been satisfied exceeds a specified threshold value. Timing constraints are composed of deadline and delay constraints for which satisfaction probabilities are defined. A confidence threshold is a minimum satisfaction probability of the timing constraint. A timing constraint is violated if the confidence threshold is not reached by the timed event trace. We present a ptime monitoring algorithm for detecting timing violation by finding the earliest expiration time (EET) of the deadline timer for each of the cases P = 100%, 50% /spl les/ P < 100%, and 0% < P < 50%, where P is the confidence threshold of the timing constraint. We give a derivation of the implicit constraints needed for computing the EET, and we show how to use an all-pairs shortest path algorithm to compute the implicit constraints.

Efficient algorithms have been developed by a number of authors to detect constraint violation or satisfaction of timed events. In extant work, the time of every event occurrence is assumed to be known exactly. However there are practical situations where we are not sure about the exact time of occurrence of an event but we may be able to capture the uncertainty by a time interval. In this paper we propose new types of timing constraints: possible and certain constraints that are pertinent to an event model where timestamps are given by time intervals. We extend previous work in timed event monitoring that is time-point based to our interval-based model. We give an efficient algorithm for monitoring timing constraints under event timing uncertainty, and sketch its proof of correctness by extending the pruning algorithm on the constraint graph to cover interval timestamps.

Timeliness is an important attribute for e-brokerage both in the detection of opportunities in a narrow time window and also in facilitating differentiation of end-to-end quality of service (QoS). In this paper, we demonstrate how real-time event monitoring techniques can be applied to time-critical e-brokerage. We start with a formal timed event model which provides the semantics for specifying complex timing correlation rules in composite events. The formal event model of the system is based on RTL (Real Time Logic), which is important for disambiguating informal specifications when multiple instances of the same event type may appear in a timing correlation rule involving composite events. This leads to our design of an e-brokerage, online stock monitoring and alerting system which enables users to express their complex preferences and provides an alert service to users in a timely manner. The user's preferences are monitored by a real-time event monitor. We report the design of this system and some performance data characterizing some key timing parameters. Our system is being used by a class of MBA students in a field test.

The spatial query has been processed in two steps, the filter step and the refinement step, due to the large volume and high complexity of the spatial data. However, this approach has been considered only in the query execution phase after completion of the query optimization phase. This paper presents query optimization strategies which take the characteristics of spatial databases into account. The first strategy is the separation of filter and refinement steps not in the query execution phase but in the query optimization phase. As the second strategy, several refinement operations can be combined in processing a complex query, and as the third strategy several filter operations can also be combined. We call the optimization technique utilizing these strategies the early separated filter and refinement (ESFAR). This paper also presents a rule-based optimization technique for ESFAR.